Overview

Dataset statistics

Number of variables24
Number of observations20000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory192.0 B

Variable types

Numeric12
Categorical9
Text1
DateTime2

Alerts

Coordenadas Geográficas has unique valuesUnique
Área Total (em metros quadrados) has unique valuesUnique
Taxas de Juros Atuais has unique valuesUnique
Custos de Manutenção Anuais has unique valuesUnique
Taxas de Condomínio Mensais has unique valuesUnique
Impostos sobre a Propriedade has unique valuesUnique
Histórico de Valorização has unique valuesUnique
Fluxos de Caixa Anuais has unique valuesUnique
Retorno sobre Investimento (ROI) has unique valuesUnique

Reproduction

Analysis started2023-09-05 22:46:34.546180
Analysis finished2023-09-05 22:47:02.377548
Duration27.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ID da Propriedade
Real number (ℝ)

Distinct8631
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4979.7729
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:02.583050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile488.95
Q12467
median4957
Q37508
95-th percentile9505
Maximum10000
Range9999
Interquartile range (IQR)5041

Descriptive statistics

Standard deviation2894.8548
Coefficient of variation (CV)0.58132266
Kurtosis-1.2071591
Mean4979.7729
Median Absolute Deviation (MAD)2518
Skewness0.0075302742
Sum99595458
Variance8380184.4
MonotonicityNot monotonic
2023-09-05T19:47:02.806488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3878 10
 
0.1%
7735 9
 
< 0.1%
6259 8
 
< 0.1%
4640 8
 
< 0.1%
866 8
 
< 0.1%
2876 7
 
< 0.1%
7721 7
 
< 0.1%
1438 7
 
< 0.1%
870 7
 
< 0.1%
7189 7
 
< 0.1%
Other values (8621) 19922
99.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 4
< 0.1%
3 5
< 0.1%
5 3
< 0.1%
6 3
< 0.1%
7 3
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
10000 3
< 0.1%
9999 1
 
< 0.1%
9998 1
 
< 0.1%
9997 3
< 0.1%
9995 1
 
< 0.1%
9993 4
< 0.1%
9992 2
< 0.1%
9991 2
< 0.1%
9990 2
< 0.1%
9988 1
 
< 0.1%

Tipo de Imóvel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Condomínio
6693 
Casa
6672 
Apartamento
6635 

Length

Max length11
Median length10
Mean length8.33015
Min length4

Characters and Unicode

Total characters166603
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApartamento
2nd rowCondomínio
3rd rowCasa
4th rowCondomínio
5th rowCondomínio

Common Values

ValueCountFrequency (%)
Condomínio 6693
33.5%
Casa 6672
33.4%
Apartamento 6635
33.2%

Length

2023-09-05T19:47:03.012656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T19:47:03.196985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
condomínio 6693
33.5%
casa 6672
33.4%
apartamento 6635
33.2%

Most occurring characters

ValueCountFrequency (%)
o 26714
16.0%
a 26614
16.0%
n 20021
12.0%
C 13365
8.0%
m 13328
8.0%
t 13270
8.0%
d 6693
 
4.0%
í 6693
 
4.0%
i 6693
 
4.0%
s 6672
 
4.0%
Other values (4) 26540
15.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 146603
88.0%
Uppercase Letter 20000
 
12.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 26714
18.2%
a 26614
18.2%
n 20021
13.7%
m 13328
9.1%
t 13270
9.1%
d 6693
 
4.6%
í 6693
 
4.6%
i 6693
 
4.6%
s 6672
 
4.6%
p 6635
 
4.5%
Other values (2) 13270
9.1%
Uppercase Letter
ValueCountFrequency (%)
C 13365
66.8%
A 6635
33.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 166603
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 26714
16.0%
a 26614
16.0%
n 20021
12.0%
C 13365
8.0%
m 13328
8.0%
t 13270
8.0%
d 6693
 
4.0%
í 6693
 
4.0%
i 6693
 
4.0%
s 6672
 
4.0%
Other values (4) 26540
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159910
96.0%
None 6693
 
4.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 26714
16.7%
a 26614
16.6%
n 20021
12.5%
C 13365
8.4%
m 13328
8.3%
t 13270
8.3%
d 6693
 
4.2%
i 6693
 
4.2%
s 6672
 
4.2%
A 6635
 
4.1%
Other values (3) 19905
12.4%
None
ValueCountFrequency (%)
í 6693
100.0%

Localização
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Cidade 5
2040 
Cidade 7
2035 
Cidade 8
2031 
Cidade 10
2025 
Cidade 9
2009 
Other values (5)
9860 

Length

Max length9
Median length8
Mean length8.10125
Min length8

Characters and Unicode

Total characters162025
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCidade 8
2nd rowCidade 7
3rd rowCidade 8
4th rowCidade 8
5th rowCidade 5

Common Values

ValueCountFrequency (%)
Cidade 5 2040
10.2%
Cidade 7 2035
10.2%
Cidade 8 2031
10.2%
Cidade 10 2025
10.1%
Cidade 9 2009
10.0%
Cidade 2 2004
10.0%
Cidade 3 1985
9.9%
Cidade 6 1973
9.9%
Cidade 4 1962
9.8%
Cidade 1 1936
9.7%

Length

2023-09-05T19:47:03.383502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T19:47:03.584964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cidade 20000
50.0%
5 2040
 
5.1%
7 2035
 
5.1%
8 2031
 
5.1%
10 2025
 
5.1%
9 2009
 
5.0%
2 2004
 
5.0%
3 1985
 
5.0%
6 1973
 
4.9%
4 1962
 
4.9%

Most occurring characters

ValueCountFrequency (%)
d 40000
24.7%
C 20000
12.3%
i 20000
12.3%
a 20000
12.3%
e 20000
12.3%
20000
12.3%
1 3961
 
2.4%
5 2040
 
1.3%
7 2035
 
1.3%
8 2031
 
1.3%
Other values (6) 11958
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100000
61.7%
Decimal Number 22025
 
13.6%
Uppercase Letter 20000
 
12.3%
Space Separator 20000
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3961
18.0%
5 2040
9.3%
7 2035
9.2%
8 2031
9.2%
0 2025
9.2%
9 2009
9.1%
2 2004
9.1%
3 1985
9.0%
6 1973
9.0%
4 1962
8.9%
Lowercase Letter
ValueCountFrequency (%)
d 40000
40.0%
i 20000
20.0%
a 20000
20.0%
e 20000
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 20000
100.0%
Space Separator
ValueCountFrequency (%)
20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 120000
74.1%
Common 42025
 
25.9%

Most frequent character per script

Common
ValueCountFrequency (%)
20000
47.6%
1 3961
 
9.4%
5 2040
 
4.9%
7 2035
 
4.8%
8 2031
 
4.8%
0 2025
 
4.8%
9 2009
 
4.8%
2 2004
 
4.8%
3 1985
 
4.7%
6 1973
 
4.7%
Latin
ValueCountFrequency (%)
d 40000
33.3%
C 20000
16.7%
i 20000
16.7%
a 20000
16.7%
e 20000
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 40000
24.7%
C 20000
12.3%
i 20000
12.3%
a 20000
12.3%
e 20000
12.3%
20000
12.3%
1 3961
 
2.4%
5 2040
 
1.3%
7 2035
 
1.3%
8 2031
 
1.3%
Other values (6) 11958
 
7.4%
Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:03.854944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length43
Mean length39.6498
Min length34

Characters and Unicode

Total characters792996
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20000 ?
Unique (%)100.0%

Sample

1st row(-70.22159224490184, 3.7662768709245995)
2nd row(61.07885709193957, 9.97770412467574)
3rd row(32.30897809624264, 63.624690308488766)
4th row(83.87940579353597, 26.253839203995028)
5th row(-12.467080314031179, 67.49621514853379)
ValueCountFrequency (%)
70.22159224490184 1
 
< 0.1%
15.74141978357278 1
 
< 0.1%
161.69079770730303 1
 
< 0.1%
51.663815973710115 1
 
< 0.1%
160.86302850822284 1
 
< 0.1%
66.59911124548321 1
 
< 0.1%
166.12624342436015 1
 
< 0.1%
39.74314187082672 1
 
< 0.1%
92.1125748716351 1
 
< 0.1%
12.56883642175697 1
 
< 0.1%
Other values (39990) 39990
> 99.9%
2023-09-05T19:47:04.280805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 72898
9.2%
5 67393
8.5%
4 66792
8.4%
6 66412
8.4%
3 65675
8.3%
2 65480
8.3%
7 65405
8.2%
8 64403
8.1%
9 60649
7.6%
0 57935
7.3%
Other values (6) 139954
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 653042
82.4%
Other Punctuation 60000
 
7.6%
Open Punctuation 20000
 
2.5%
Space Separator 20000
 
2.5%
Close Punctuation 20000
 
2.5%
Dash Punctuation 19954
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 72898
11.2%
5 67393
10.3%
4 66792
10.2%
6 66412
10.2%
3 65675
10.1%
2 65480
10.0%
7 65405
10.0%
8 64403
9.9%
9 60649
9.3%
0 57935
8.9%
Other Punctuation
ValueCountFrequency (%)
. 40000
66.7%
, 20000
33.3%
Open Punctuation
ValueCountFrequency (%)
( 20000
100.0%
Space Separator
ValueCountFrequency (%)
20000
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19954
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 792996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 72898
9.2%
5 67393
8.5%
4 66792
8.4%
6 66412
8.4%
3 65675
8.3%
2 65480
8.3%
7 65405
8.2%
8 64403
8.1%
9 60649
7.6%
0 57935
7.3%
Other values (6) 139954
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 792996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 72898
9.2%
5 67393
8.5%
4 66792
8.4%
6 66412
8.4%
3 65675
8.3%
2 65480
8.3%
7 65405
8.2%
8 64403
8.1%
9 60649
7.6%
0 57935
7.3%
Other values (6) 139954
17.6%

Número de Quartos
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.48765
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:04.463896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7186318
Coefficient of variation (CV)0.49277645
Kurtosis-1.2867706
Mean3.48765
Median Absolute Deviation (MAD)2
Skewness0.014361651
Sum69753
Variance2.9536952
MonotonicityNot monotonic
2023-09-05T19:47:04.631470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 3419
17.1%
1 3396
17.0%
6 3373
16.9%
5 3311
16.6%
3 3278
16.4%
4 3223
16.1%
ValueCountFrequency (%)
1 3396
17.0%
2 3419
17.1%
3 3278
16.4%
4 3223
16.1%
5 3311
16.6%
6 3373
16.9%
ValueCountFrequency (%)
6 3373
16.9%
5 3311
16.6%
4 3223
16.1%
3 3278
16.4%
2 3419
17.1%
1 3396
17.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
4
5131 
1
5054 
3
4936 
2
4879 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
4 5131
25.7%
1 5054
25.3%
3 4936
24.7%
2 4879
24.4%

Length

2023-09-05T19:47:04.808623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T19:47:04.965205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 5131
25.7%
1 5054
25.3%
3 4936
24.7%
2 4879
24.4%

Most occurring characters

ValueCountFrequency (%)
4 5131
25.7%
1 5054
25.3%
3 4936
24.7%
2 4879
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 5131
25.7%
1 5054
25.3%
3 4936
24.7%
2 4879
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 5131
25.7%
1 5054
25.3%
3 4936
24.7%
2 4879
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 5131
25.7%
1 5054
25.3%
3 4936
24.7%
2 4879
24.4%

Área Total (em metros quadrados)
Real number (ℝ)

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.28529
Minimum50.001324
Maximum299.96913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:05.165267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50.001324
5-th percentile62.821664
Q1112.6974
median175.88135
Q3237.10511
95-th percentile287.75897
Maximum299.96913
Range249.96781
Interquartile range (IQR)124.40771

Descriptive statistics

Standard deviation72.085778
Coefficient of variation (CV)0.4112483
Kurtosis-1.1931871
Mean175.28529
Median Absolute Deviation (MAD)62.184445
Skewness-0.0018721972
Sum3505705.8
Variance5196.3593
MonotonicityNot monotonic
2023-09-05T19:47:05.457485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166.1873685 1
 
< 0.1%
61.5111992 1
 
< 0.1%
219.4416301 1
 
< 0.1%
227.5092651 1
 
< 0.1%
86.24110051 1
 
< 0.1%
196.3696191 1
 
< 0.1%
206.1211508 1
 
< 0.1%
213.1802684 1
 
< 0.1%
143.6695815 1
 
< 0.1%
284.8197049 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
50.00132388 1
< 0.1%
50.01853548 1
< 0.1%
50.02249973 1
< 0.1%
50.02799413 1
< 0.1%
50.0291867 1
< 0.1%
50.03902358 1
< 0.1%
50.04854964 1
< 0.1%
50.05155458 1
< 0.1%
50.06083095 1
< 0.1%
50.08350272 1
< 0.1%
ValueCountFrequency (%)
299.9691309 1
< 0.1%
299.9674472 1
< 0.1%
299.9528996 1
< 0.1%
299.9338467 1
< 0.1%
299.9245925 1
< 0.1%
299.9178651 1
< 0.1%
299.9155138 1
< 0.1%
299.9151753 1
< 0.1%
299.90449 1
< 0.1%
299.8899375 1
< 0.1%

Idade da Propriedade
Real number (ℝ)

Distinct50
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5706
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:05.707815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median26
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.404999
Coefficient of variation (CV)0.56334223
Kurtosis-1.1979594
Mean25.5706
Median Absolute Deviation (MAD)12
Skewness-0.00059051807
Sum511412
Variance207.50399
MonotonicityNot monotonic
2023-09-05T19:47:05.944211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 449
 
2.2%
50 430
 
2.1%
36 423
 
2.1%
15 422
 
2.1%
30 420
 
2.1%
13 419
 
2.1%
12 418
 
2.1%
32 417
 
2.1%
11 416
 
2.1%
7 415
 
2.1%
Other values (40) 15771
78.9%
ValueCountFrequency (%)
1 369
1.8%
2 405
2.0%
3 386
1.9%
4 402
2.0%
5 402
2.0%
6 371
1.9%
7 415
2.1%
8 403
2.0%
9 396
2.0%
10 369
1.8%
ValueCountFrequency (%)
50 430
2.1%
49 377
1.9%
48 411
2.1%
47 404
2.0%
46 407
2.0%
45 384
1.9%
44 410
2.1%
43 382
1.9%
42 390
1.9%
41 411
2.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Média
5067 
Boa
5048 
Excelente
4967 
Ruim
4918 

Length

Max length9
Median length5
Mean length5.2427
Min length3

Characters and Unicode

Total characters104854
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRuim
2nd rowBoa
3rd rowBoa
4th rowRuim
5th rowMédia

Common Values

ValueCountFrequency (%)
Média 5067
25.3%
Boa 5048
25.2%
Excelente 4967
24.8%
Ruim 4918
24.6%

Length

2023-09-05T19:47:06.150986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T19:47:06.436626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
média 5067
25.3%
boa 5048
25.2%
excelente 4967
24.8%
ruim 4918
24.6%

Most occurring characters

ValueCountFrequency (%)
e 14901
14.2%
a 10115
 
9.6%
i 9985
 
9.5%
M 5067
 
4.8%
d 5067
 
4.8%
é 5067
 
4.8%
B 5048
 
4.8%
o 5048
 
4.8%
l 4967
 
4.7%
t 4967
 
4.7%
Other values (7) 34622
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 84854
80.9%
Uppercase Letter 20000
 
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14901
17.6%
a 10115
11.9%
i 9985
11.8%
d 5067
 
6.0%
é 5067
 
6.0%
o 5048
 
5.9%
l 4967
 
5.9%
t 4967
 
5.9%
n 4967
 
5.9%
x 4967
 
5.9%
Other values (3) 14803
17.4%
Uppercase Letter
ValueCountFrequency (%)
M 5067
25.3%
B 5048
25.2%
E 4967
24.8%
R 4918
24.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 104854
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14901
14.2%
a 10115
 
9.6%
i 9985
 
9.5%
M 5067
 
4.8%
d 5067
 
4.8%
é 5067
 
4.8%
B 5048
 
4.8%
o 5048
 
4.8%
l 4967
 
4.7%
t 4967
 
4.7%
Other values (7) 34622
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99787
95.2%
None 5067
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14901
14.9%
a 10115
 
10.1%
i 9985
 
10.0%
M 5067
 
5.1%
d 5067
 
5.1%
B 5048
 
5.1%
o 5048
 
5.1%
l 4967
 
5.0%
t 4967
 
5.0%
n 4967
 
5.0%
Other values (6) 29655
29.7%
None
ValueCountFrequency (%)
é 5067
100.0%

Amenidades
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Vista panorâmica, Piscina
1737 
Jardim, Piscina
1706 
Piscina, Jardim
1697 
Jardim, Vista panorâmica
1687 
Garagem, Jardim
1685 
Other values (7)
11488 

Length

Max length25
Median length24
Mean length20.00335
Min length15

Characters and Unicode

Total characters400067
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGaragem, Vista panorâmica
2nd rowJardim, Garagem
3rd rowPiscina, Vista panorâmica
4th rowGaragem, Vista panorâmica
5th rowPiscina, Garagem

Common Values

ValueCountFrequency (%)
Vista panorâmica, Piscina 1737
8.7%
Jardim, Piscina 1706
8.5%
Piscina, Jardim 1697
8.5%
Jardim, Vista panorâmica 1687
8.4%
Garagem, Jardim 1685
8.4%
Jardim, Garagem 1678
8.4%
Vista panorâmica, Jardim 1678
8.4%
Piscina, Vista panorâmica 1674
8.4%
Garagem, Vista panorâmica 1662
8.3%
Garagem, Piscina 1613
8.1%
Other values (2) 3183
15.9%

Length

2023-09-05T19:47:06.626117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jardim 10131
20.3%
piscina 10026
20.0%
vista 10022
20.0%
panorâmica 10022
20.0%
garagem 9821
19.6%

Most occurring characters

ValueCountFrequency (%)
a 69865
17.5%
i 50227
12.6%
30022
 
7.5%
r 29974
 
7.5%
m 29974
 
7.5%
s 20048
 
5.0%
n 20048
 
5.0%
c 20048
 
5.0%
, 20000
 
5.0%
d 10131
 
2.5%
Other values (10) 99730
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 310045
77.5%
Uppercase Letter 40000
 
10.0%
Space Separator 30022
 
7.5%
Other Punctuation 20000
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 69865
22.5%
i 50227
16.2%
r 29974
9.7%
m 29974
9.7%
s 20048
 
6.5%
n 20048
 
6.5%
c 20048
 
6.5%
d 10131
 
3.3%
â 10022
 
3.2%
o 10022
 
3.2%
Other values (4) 39686
12.8%
Uppercase Letter
ValueCountFrequency (%)
J 10131
25.3%
P 10026
25.1%
V 10022
25.1%
G 9821
24.6%
Space Separator
ValueCountFrequency (%)
30022
100.0%
Other Punctuation
ValueCountFrequency (%)
, 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 350045
87.5%
Common 50022
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 69865
20.0%
i 50227
14.3%
r 29974
 
8.6%
m 29974
 
8.6%
s 20048
 
5.7%
n 20048
 
5.7%
c 20048
 
5.7%
d 10131
 
2.9%
J 10131
 
2.9%
P 10026
 
2.9%
Other values (8) 79573
22.7%
Common
ValueCountFrequency (%)
30022
60.0%
, 20000
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390045
97.5%
None 10022
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 69865
17.9%
i 50227
12.9%
30022
 
7.7%
r 29974
 
7.7%
m 29974
 
7.7%
s 20048
 
5.1%
n 20048
 
5.1%
c 20048
 
5.1%
, 20000
 
5.1%
d 10131
 
2.6%
Other values (9) 89708
23.0%
None
ValueCountFrequency (%)
â 10022
100.0%

Preço de Venda Anterior
Real number (ℝ)

Distinct19953
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2527320.8
Minimum50332
Maximum4999877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:06.819088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50332
5-th percentile307307.3
Q11298816.2
median2528174.5
Q33762439.5
95-th percentile4748674.4
Maximum4999877
Range4949545
Interquartile range (IQR)2463623.2

Descriptive statistics

Standard deviation1422882
Coefficient of variation (CV)0.56300017
Kurtosis-1.1942864
Mean2527320.8
Median Absolute Deviation (MAD)1231544
Skewness0.0036538082
Sum5.0546416 × 1010
Variance2.0245933 × 1012
MonotonicityNot monotonic
2023-09-05T19:47:07.037912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
946757 2
 
< 0.1%
4754029 2
 
< 0.1%
4305339 2
 
< 0.1%
3869232 2
 
< 0.1%
109617 2
 
< 0.1%
1666854 2
 
< 0.1%
2353639 2
 
< 0.1%
2674035 2
 
< 0.1%
561985 2
 
< 0.1%
2526327 2
 
< 0.1%
Other values (19943) 19980
99.9%
ValueCountFrequency (%)
50332 1
< 0.1%
50781 1
< 0.1%
51793 1
< 0.1%
51806 1
< 0.1%
52042 1
< 0.1%
52056 1
< 0.1%
52091 1
< 0.1%
52152 1
< 0.1%
52250 1
< 0.1%
52748 1
< 0.1%
ValueCountFrequency (%)
4999877 1
< 0.1%
4999756 1
< 0.1%
4999665 1
< 0.1%
4999609 1
< 0.1%
4999523 1
< 0.1%
4999449 1
< 0.1%
4999356 1
< 0.1%
4999044 1
< 0.1%
4998600 1
< 0.1%
4997895 1
< 0.1%
Distinct10962
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Minimum1980-01-01 00:00:00
Maximum2023-12-28 00:00:00
2023-09-05T19:47:07.237746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:07.451176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Não
10096 
Sim
9904 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão
2nd rowNão
3rd rowNão
4th rowSim
5th rowSim

Common Values

ValueCountFrequency (%)
Não 10096
50.5%
Sim 9904
49.5%

Length

2023-09-05T19:47:07.649645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T19:47:07.802238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
não 10096
50.5%
sim 9904
49.5%

Most occurring characters

ValueCountFrequency (%)
N 10096
16.8%
ã 10096
16.8%
o 10096
16.8%
S 9904
16.5%
i 9904
16.5%
m 9904
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40000
66.7%
Uppercase Letter 20000
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
ã 10096
25.2%
o 10096
25.2%
i 9904
24.8%
m 9904
24.8%
Uppercase Letter
ValueCountFrequency (%)
N 10096
50.5%
S 9904
49.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 60000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 10096
16.8%
ã 10096
16.8%
o 10096
16.8%
S 9904
16.5%
i 9904
16.5%
m 9904
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49904
83.2%
None 10096
 
16.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 10096
20.2%
o 10096
20.2%
S 9904
19.8%
i 9904
19.8%
m 9904
19.8%
None
ValueCountFrequency (%)
ã 10096
100.0%
Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Bairro 8
 
1069
Bairro 17
 
1026
Bairro 18
 
1025
Bairro 6
 
1023
Bairro 10
 
1010
Other values (15)
14847 

Length

Max length9
Median length9
Mean length8.54835
Min length8

Characters and Unicode

Total characters170967
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBairro 12
2nd rowBairro 9
3rd rowBairro 16
4th rowBairro 15
5th rowBairro 8

Common Values

ValueCountFrequency (%)
Bairro 8 1069
 
5.3%
Bairro 17 1026
 
5.1%
Bairro 18 1025
 
5.1%
Bairro 6 1023
 
5.1%
Bairro 10 1010
 
5.1%
Bairro 9 1009
 
5.0%
Bairro 13 1008
 
5.0%
Bairro 7 1004
 
5.0%
Bairro 2 1001
 
5.0%
Bairro 15 1001
 
5.0%
Other values (10) 9824
49.1%

Length

2023-09-05T19:47:07.962808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bairro 20000
50.0%
8 1069
 
2.7%
17 1026
 
2.6%
18 1025
 
2.6%
6 1023
 
2.6%
10 1010
 
2.5%
9 1009
 
2.5%
13 1008
 
2.5%
7 1004
 
2.5%
15 1001
 
2.5%
Other values (11) 10825
27.1%

Most occurring characters

ValueCountFrequency (%)
r 40000
23.4%
B 20000
11.7%
a 20000
11.7%
i 20000
11.7%
o 20000
11.7%
20000
11.7%
1 11954
 
7.0%
2 2971
 
1.7%
8 2094
 
1.2%
7 2030
 
1.2%
Other values (6) 11918
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100000
58.5%
Decimal Number 30967
 
18.1%
Uppercase Letter 20000
 
11.7%
Space Separator 20000
 
11.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11954
38.6%
2 2971
 
9.6%
8 2094
 
6.8%
7 2030
 
6.6%
5 1996
 
6.4%
6 1994
 
6.4%
0 1994
 
6.4%
9 1993
 
6.4%
3 1972
 
6.4%
4 1969
 
6.4%
Lowercase Letter
ValueCountFrequency (%)
r 40000
40.0%
a 20000
20.0%
i 20000
20.0%
o 20000
20.0%
Uppercase Letter
ValueCountFrequency (%)
B 20000
100.0%
Space Separator
ValueCountFrequency (%)
20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 120000
70.2%
Common 50967
29.8%

Most frequent character per script

Common
ValueCountFrequency (%)
20000
39.2%
1 11954
23.5%
2 2971
 
5.8%
8 2094
 
4.1%
7 2030
 
4.0%
5 1996
 
3.9%
6 1994
 
3.9%
0 1994
 
3.9%
9 1993
 
3.9%
3 1972
 
3.9%
Latin
ValueCountFrequency (%)
r 40000
33.3%
B 20000
16.7%
a 20000
16.7%
i 20000
16.7%
o 20000
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 40000
23.4%
B 20000
11.7%
a 20000
11.7%
i 20000
11.7%
o 20000
11.7%
20000
11.7%
1 11954
 
7.0%
2 2971
 
1.7%
8 2094
 
1.2%
7 2030
 
1.2%
Other values (6) 11918
 
7.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Decrescente
6767 
Estável
6640 
Crescente
6593 

Length

Max length11
Median length9
Mean length9.0127
Min length7

Characters and Unicode

Total characters180254
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDecrescente
2nd rowEstável
3rd rowCrescente
4th rowEstável
5th rowDecrescente

Common Values

ValueCountFrequency (%)
Decrescente 6767
33.8%
Estável 6640
33.2%
Crescente 6593
33.0%

Length

2023-09-05T19:47:08.211655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T19:47:08.436781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
decrescente 6767
33.8%
estável 6640
33.2%
crescente 6593
33.0%

Most occurring characters

ValueCountFrequency (%)
e 53487
29.7%
c 20127
 
11.2%
s 20000
 
11.1%
t 20000
 
11.1%
r 13360
 
7.4%
n 13360
 
7.4%
D 6767
 
3.8%
E 6640
 
3.7%
á 6640
 
3.7%
v 6640
 
3.7%
Other values (2) 13233
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 160254
88.9%
Uppercase Letter 20000
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 53487
33.4%
c 20127
 
12.6%
s 20000
 
12.5%
t 20000
 
12.5%
r 13360
 
8.3%
n 13360
 
8.3%
á 6640
 
4.1%
v 6640
 
4.1%
l 6640
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
D 6767
33.8%
E 6640
33.2%
C 6593
33.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 180254
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 53487
29.7%
c 20127
 
11.2%
s 20000
 
11.1%
t 20000
 
11.1%
r 13360
 
7.4%
n 13360
 
7.4%
D 6767
 
3.8%
E 6640
 
3.7%
á 6640
 
3.7%
v 6640
 
3.7%
Other values (2) 13233
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173614
96.3%
None 6640
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 53487
30.8%
c 20127
 
11.6%
s 20000
 
11.5%
t 20000
 
11.5%
r 13360
 
7.7%
n 13360
 
7.7%
D 6767
 
3.9%
E 6640
 
3.8%
v 6640
 
3.8%
l 6640
 
3.8%
None
ValueCountFrequency (%)
á 6640
100.0%

Taxas de Juros Atuais
Real number (ℝ)

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9976546
Minimum2.0001895
Maximum5.9997337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:08.670429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0001895
5-th percentile2.2050171
Q12.9860352
median3.9980898
Q34.999098
95-th percentile5.7958974
Maximum5.9997337
Range3.9995442
Interquartile range (IQR)2.0130628

Descriptive statistics

Standard deviation1.1553953
Coefficient of variation (CV)0.28901829
Kurtosis-1.2087333
Mean3.9976546
Median Absolute Deviation (MAD)1.0057514
Skewness0.00082019466
Sum79953.093
Variance1.3349383
MonotonicityNot monotonic
2023-09-05T19:47:08.921761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.32830225 1
 
< 0.1%
3.08968716 1
 
< 0.1%
4.620953305 1
 
< 0.1%
4.764526452 1
 
< 0.1%
2.999725869 1
 
< 0.1%
4.352659649 1
 
< 0.1%
3.059628096 1
 
< 0.1%
5.776080111 1
 
< 0.1%
5.345173021 1
 
< 0.1%
4.617845444 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
2.000189463 1
< 0.1%
2.000317137 1
< 0.1%
2.000384133 1
< 0.1%
2.000397261 1
< 0.1%
2.000600658 1
< 0.1%
2.000609274 1
< 0.1%
2.00068998 1
< 0.1%
2.000810133 1
< 0.1%
2.001027439 1
< 0.1%
2.001085625 1
< 0.1%
ValueCountFrequency (%)
5.999733685 1
< 0.1%
5.999646202 1
< 0.1%
5.999168355 1
< 0.1%
5.999156511 1
< 0.1%
5.999107651 1
< 0.1%
5.998897642 1
< 0.1%
5.998762899 1
< 0.1%
5.998579411 1
< 0.1%
5.998326169 1
< 0.1%
5.998178819 1
< 0.1%

Custos de Manutenção Anuais
Real number (ℝ)

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2549.6743
Minimum100.43171
Maximum4999.9332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:09.140473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100.43171
5-th percentile355.44858
Q11326.8962
median2550.9097
Q33776.1689
95-th percentile4750.0524
Maximum4999.9332
Range4899.5014
Interquartile range (IQR)2449.2727

Descriptive statistics

Standard deviation1412.2397
Coefficient of variation (CV)0.55389023
Kurtosis-1.2025753
Mean2549.6743
Median Absolute Deviation (MAD)1224.8513
Skewness0.00062652899
Sum50993486
Variance1994420.9
MonotonicityNot monotonic
2023-09-05T19:47:09.412717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3239.872395 1
 
< 0.1%
3859.527441 1
 
< 0.1%
993.8869631 1
 
< 0.1%
2113.983387 1
 
< 0.1%
1821.207338 1
 
< 0.1%
3326.368207 1
 
< 0.1%
2705.397204 1
 
< 0.1%
2090.341132 1
 
< 0.1%
750.9691611 1
 
< 0.1%
3741.619367 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
100.4317112 1
< 0.1%
100.7134358 1
< 0.1%
101.0401406 1
< 0.1%
101.5823321 1
< 0.1%
101.9712357 1
< 0.1%
102.8875474 1
< 0.1%
102.8903438 1
< 0.1%
103.0942971 1
< 0.1%
103.1116995 1
< 0.1%
103.3993575 1
< 0.1%
ValueCountFrequency (%)
4999.933161 1
< 0.1%
4999.136461 1
< 0.1%
4998.444024 1
< 0.1%
4998.381763 1
< 0.1%
4998.162872 1
< 0.1%
4998.150319 1
< 0.1%
4997.549047 1
< 0.1%
4997.227665 1
< 0.1%
4997.038213 1
< 0.1%
4996.756934 1
< 0.1%

Taxas de Condomínio Mensais
Real number (ℝ)

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.81418
Minimum50.057598
Maximum499.96606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:09.685986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50.057598
5-th percentile72.238422
Q1161.93693
median274.83756
Q3386.3815
95-th percentile477.20976
Maximum499.96606
Range449.90846
Interquartile range (IQR)224.44457

Descriptive statistics

Standard deviation129.64122
Coefficient of variation (CV)0.47174137
Kurtosis-1.196181
Mean274.81418
Median Absolute Deviation (MAD)112.36853
Skewness0.00044640892
Sum5496283.5
Variance16806.845
MonotonicityNot monotonic
2023-09-05T19:47:09.956059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.978276 1
 
< 0.1%
308.7743372 1
 
< 0.1%
307.3780507 1
 
< 0.1%
236.3257497 1
 
< 0.1%
122.3502178 1
 
< 0.1%
428.8273032 1
 
< 0.1%
197.6422183 1
 
< 0.1%
89.29853968 1
 
< 0.1%
161.0024876 1
 
< 0.1%
259.4888499 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
50.05759755 1
< 0.1%
50.12576468 1
< 0.1%
50.12903503 1
< 0.1%
50.17190152 1
< 0.1%
50.189158 1
< 0.1%
50.24000554 1
< 0.1%
50.2510003 1
< 0.1%
50.25700124 1
< 0.1%
50.28219356 1
< 0.1%
50.28453324 1
< 0.1%
ValueCountFrequency (%)
499.9660614 1
< 0.1%
499.9212625 1
< 0.1%
499.8855984 1
< 0.1%
499.8797252 1
< 0.1%
499.8687771 1
< 0.1%
499.8500659 1
< 0.1%
499.8101126 1
< 0.1%
499.7469788 1
< 0.1%
499.723801 1
< 0.1%
499.6960606 1
< 0.1%

Impostos sobre a Propriedade
Real number (ℝ)

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2761.8056
Minimum500.25817
Maximum4999.6644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:10.197143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500.25817
5-th percentile719.12727
Q11635.01
median2773.9856
Q33888.5422
95-th percentile4779.083
Maximum4999.6644
Range4499.4063
Interquartile range (IQR)2253.5321

Descriptive statistics

Standard deviation1302.7006
Coefficient of variation (CV)0.47168438
Kurtosis-1.1997794
Mean2761.8056
Median Absolute Deviation (MAD)1129.1849
Skewness-0.01376019
Sum55236112
Variance1697028.8
MonotonicityNot monotonic
2023-09-05T19:47:10.426456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2032.27034 1
 
< 0.1%
4543.130497 1
 
< 0.1%
1631.933989 1
 
< 0.1%
1552.14976 1
 
< 0.1%
1598.823007 1
 
< 0.1%
4813.062336 1
 
< 0.1%
2246.188305 1
 
< 0.1%
4953.225935 1
 
< 0.1%
4597.788005 1
 
< 0.1%
1606.88139 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
500.2581696 1
< 0.1%
500.3012001 1
< 0.1%
500.3354058 1
< 0.1%
501.7021139 1
< 0.1%
501.9914325 1
< 0.1%
502.0815228 1
< 0.1%
502.2172414 1
< 0.1%
502.2766659 1
< 0.1%
502.2996779 1
< 0.1%
502.3060176 1
< 0.1%
ValueCountFrequency (%)
4999.664433 1
< 0.1%
4999.565338 1
< 0.1%
4999.311752 1
< 0.1%
4999.002217 1
< 0.1%
4998.76776 1
< 0.1%
4998.449428 1
< 0.1%
4998.120233 1
< 0.1%
4997.615892 1
< 0.1%
4997.29817 1
< 0.1%
4997.228759 1
< 0.1%

Histórico de Valorização
Real number (ℝ)

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30149382
Minimum0.10003267
Maximum0.49994545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:10.719672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.10003267
5-th percentile0.12028655
Q10.20080738
median0.30212886
Q30.40275421
95-th percentile0.4800477
Maximum0.49994545
Range0.39991278
Interquartile range (IQR)0.20194683

Descriptive statistics

Standard deviation0.11580921
Coefficient of variation (CV)0.38411803
Kurtosis-1.2059973
Mean0.30149382
Median Absolute Deviation (MAD)0.10099812
Skewness-0.021701667
Sum6029.8764
Variance0.013411774
MonotonicityNot monotonic
2023-09-05T19:47:11.004909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.302762487 1
 
< 0.1%
0.2591587873 1
 
< 0.1%
0.4648408829 1
 
< 0.1%
0.1098509006 1
 
< 0.1%
0.240335933 1
 
< 0.1%
0.2369120919 1
 
< 0.1%
0.2591631955 1
 
< 0.1%
0.3414982968 1
 
< 0.1%
0.3879006244 1
 
< 0.1%
0.3262976105 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
0.1000326738 1
< 0.1%
0.1000343172 1
< 0.1%
0.1000728789 1
< 0.1%
0.1000737122 1
< 0.1%
0.1000840179 1
< 0.1%
0.1001006709 1
< 0.1%
0.1001076046 1
< 0.1%
0.1001365503 1
< 0.1%
0.1001604764 1
< 0.1%
0.1001641435 1
< 0.1%
ValueCountFrequency (%)
0.4999454506 1
< 0.1%
0.4999427359 1
< 0.1%
0.4999307119 1
< 0.1%
0.4999152197 1
< 0.1%
0.499864722 1
< 0.1%
0.4998355515 1
< 0.1%
0.4997153094 1
< 0.1%
0.499704207 1
< 0.1%
0.4996977208 1
< 0.1%
0.4996878827 1
< 0.1%

Fluxos de Caixa Anuais
Real number (ℝ)

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25422.182
Minimum1003.5263
Maximum49999.887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:11.272194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1003.5263
5-th percentile3436.7144
Q113284.907
median25271.364
Q337660.387
95-th percentile47515.454
Maximum49999.887
Range48996.36
Interquartile range (IQR)24375.48

Descriptive statistics

Standard deviation14123.381
Coefficient of variation (CV)0.55555344
Kurtosis-1.1982144
Mean25422.182
Median Absolute Deviation (MAD)12180.48
Skewness0.011603141
Sum5.0844364 × 108
Variance1.9946988 × 108
MonotonicityNot monotonic
2023-09-05T19:47:11.491749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2244.279068 1
 
< 0.1%
38770.5367 1
 
< 0.1%
26310.07429 1
 
< 0.1%
19854.90008 1
 
< 0.1%
30841.97595 1
 
< 0.1%
14410.43933 1
 
< 0.1%
9485.411553 1
 
< 0.1%
4037.742351 1
 
< 0.1%
39408.15784 1
 
< 0.1%
35533.76929 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
1003.526278 1
< 0.1%
1004.423475 1
< 0.1%
1005.403045 1
< 0.1%
1005.435244 1
< 0.1%
1008.284248 1
< 0.1%
1009.598938 1
< 0.1%
1010.825307 1
< 0.1%
1017.659496 1
< 0.1%
1022.846001 1
< 0.1%
1024.268087 1
< 0.1%
ValueCountFrequency (%)
49999.88676 1
< 0.1%
49990.03278 1
< 0.1%
49986.37153 1
< 0.1%
49981.37457 1
< 0.1%
49977.85994 1
< 0.1%
49975.38484 1
< 0.1%
49969.80285 1
< 0.1%
49962.47207 1
< 0.1%
49962.37472 1
< 0.1%
49955.73554 1
< 0.1%

Retorno sobre Investimento (ROI)
Real number (ℝ)

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12494099
Minimum0.050002605
Maximum0.19999086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-09-05T19:47:11.825635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.050002605
5-th percentile0.057335876
Q10.087384954
median0.1247634
Q30.16307368
95-th percentile0.19243134
Maximum0.19999086
Range0.14998826
Interquartile range (IQR)0.075688729

Descriptive statistics

Standard deviation0.04348953
Coefficient of variation (CV)0.34808057
Kurtosis-1.2167766
Mean0.12494099
Median Absolute Deviation (MAD)0.037849784
Skewness-0.0047301929
Sum2498.8198
Variance0.0018913392
MonotonicityNot monotonic
2023-09-05T19:47:12.069581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05966695579 1
 
< 0.1%
0.1206280576 1
 
< 0.1%
0.06567406597 1
 
< 0.1%
0.1012648642 1
 
< 0.1%
0.1670529482 1
 
< 0.1%
0.1603842594 1
 
< 0.1%
0.09558127671 1
 
< 0.1%
0.1269345088 1
 
< 0.1%
0.188290692 1
 
< 0.1%
0.07025404501 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
0.05000260538 1
< 0.1%
0.05000569428 1
< 0.1%
0.0500131765 1
< 0.1%
0.05002822648 1
< 0.1%
0.05002849211 1
< 0.1%
0.05003099284 1
< 0.1%
0.050044856 1
< 0.1%
0.05005168826 1
< 0.1%
0.05006107701 1
< 0.1%
0.05007116285 1
< 0.1%
ValueCountFrequency (%)
0.199990862 1
< 0.1%
0.199988173 1
< 0.1%
0.1999801234 1
< 0.1%
0.1999753151 1
< 0.1%
0.1999744802 1
< 0.1%
0.1999733013 1
< 0.1%
0.1999712106 1
< 0.1%
0.1999645355 1
< 0.1%
0.1999472173 1
< 0.1%
0.1999405651 1
< 0.1%
Distinct4637
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Minimum2010-01-01 00:00:00
Maximum2023-12-28 00:00:00
2023-09-05T19:47:12.287765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:12.494213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Fonte dos Dados
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Zillow
6732 
Kaggle
6637 
Redfin
6631 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters120000
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKaggle
2nd rowRedfin
3rd rowZillow
4th rowZillow
5th rowZillow

Common Values

ValueCountFrequency (%)
Zillow 6732
33.7%
Kaggle 6637
33.2%
Redfin 6631
33.2%

Length

2023-09-05T19:47:12.681381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T19:47:12.832946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
zillow 6732
33.7%
kaggle 6637
33.2%
redfin 6631
33.2%

Most occurring characters

ValueCountFrequency (%)
l 20101
16.8%
i 13363
11.1%
g 13274
11.1%
e 13268
11.1%
Z 6732
 
5.6%
o 6732
 
5.6%
w 6732
 
5.6%
K 6637
 
5.5%
a 6637
 
5.5%
R 6631
 
5.5%
Other values (3) 19893
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100000
83.3%
Uppercase Letter 20000
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 20101
20.1%
i 13363
13.4%
g 13274
13.3%
e 13268
13.3%
o 6732
 
6.7%
w 6732
 
6.7%
a 6637
 
6.6%
d 6631
 
6.6%
f 6631
 
6.6%
n 6631
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
Z 6732
33.7%
K 6637
33.2%
R 6631
33.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 120000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 20101
16.8%
i 13363
11.1%
g 13274
11.1%
e 13268
11.1%
Z 6732
 
5.6%
o 6732
 
5.6%
w 6732
 
5.6%
K 6637
 
5.5%
a 6637
 
5.5%
R 6631
 
5.5%
Other values (3) 19893
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 20101
16.8%
i 13363
11.1%
g 13274
11.1%
e 13268
11.1%
Z 6732
 
5.6%
o 6732
 
5.6%
w 6732
 
5.6%
K 6637
 
5.5%
a 6637
 
5.5%
R 6631
 
5.5%
Other values (3) 19893
16.6%

Interactions

2023-09-05T19:46:59.340017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:38.033751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:39.845699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:41.830775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:43.887257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:45.738087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:47.492809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:49.452291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:51.491129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:53.450616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:55.339591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:57.462224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:59.489596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:38.192126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:40.024223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:41.982370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:44.026884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:45.874749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:47.640414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:49.593883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:51.641979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:53.593941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:55.499164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:57.623792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:59.856960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:38.336856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:40.159844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:42.138491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:44.173982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:46.016371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:47.795999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:49.753457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:51.821529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:53.749496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:55.728430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:57.773420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:00.009515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:38.487802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:40.313433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:42.317543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:44.312616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:46.159955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:47.949559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:49.896076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:51.975090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:53.899125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:55.889001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:57.916546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:00.165822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:38.634067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:40.453060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:42.457712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:44.449235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:46.302907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:48.100973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:50.044038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:52.176037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:54.049736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:56.046580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:58.079590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:00.316419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:38.775993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:40.597835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:42.610305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:44.627730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:46.444528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:48.250572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:50.183956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:52.329430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:54.196302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:56.200191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:58.230213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:00.490734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:38.940098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:40.838192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:42.774864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:44.822210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:46.601686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:48.413627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:50.345282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:52.501997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:54.379840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:56.373705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:58.410577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:00.643356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:39.090193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:40.983831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:42.970341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:44.979788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:46.749803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:48.614759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:50.521384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:52.654561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:54.522458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:56.556217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:58.566497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:00.789934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:39.233307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:41.127419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:43.117947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:45.130388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:46.889103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:48.771340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:50.676317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:52.794187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:54.669039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:56.720777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:58.714650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:00.934564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:39.369972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:41.259095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:43.290580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:45.268025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:47.023742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:48.941628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:50.819954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:52.928859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:54.852547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:56.884400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:58.858255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:01.108099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:39.535499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:41.419668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:43.573175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:45.436574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:47.187567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:49.124140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:50.990496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:53.097377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:55.021468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:57.089221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:59.041765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:47:01.272672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:39.686097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:41.659573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:43.726686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:45.574521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:47.335173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:49.280728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:51.141065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:53.277895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:55.171129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:57.273729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-05T19:46:59.178404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-05T19:47:12.982478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ID da PropriedadeNúmero de QuartosÁrea Total (em metros quadrados)Idade da PropriedadePreço de Venda AnteriorTaxas de Juros AtuaisCustos de Manutenção AnuaisTaxas de Condomínio MensaisImpostos sobre a PropriedadeHistórico de ValorizaçãoFluxos de Caixa AnuaisRetorno sobre Investimento (ROI)Tipo de ImóvelLocalizaçãoNúmero de BanheirosCondição da PropriedadeAmenidadesHistórico de AluguelCaracterísticas do BairroTendências de MercadoFonte dos Dados
ID da Propriedade1.000-0.0030.003-0.0070.002-0.0030.0100.0040.004-0.006-0.0150.0150.0000.0070.0060.0000.0090.0000.0000.0000.000
Número de Quartos-0.0031.000-0.003-0.001-0.0010.0000.006-0.0060.0070.008-0.0030.0040.0110.0000.0070.0010.0000.0000.0000.0000.005
Área Total (em metros quadrados)0.003-0.0031.0000.0140.0090.0110.0050.010-0.006-0.003-0.004-0.0000.0010.0020.0080.0080.0000.0000.0030.0000.012
Idade da Propriedade-0.007-0.0010.0141.0000.013-0.005-0.0130.008-0.0040.012-0.0040.0020.0100.0000.0000.0000.0070.0000.0080.0180.012
Preço de Venda Anterior0.002-0.0010.0090.0131.000-0.0060.010-0.0010.008-0.0040.006-0.0040.0130.0110.0000.0000.0000.0000.0110.0000.009
Taxas de Juros Atuais-0.0030.0000.011-0.005-0.0061.000-0.0080.006-0.006-0.004-0.0120.0070.0050.0000.0000.0000.0120.0070.0020.0000.000
Custos de Manutenção Anuais0.0100.0060.005-0.0130.010-0.0081.0000.015-0.014-0.002-0.0050.0060.0000.0070.0190.0000.0000.0100.0110.0120.015
Taxas de Condomínio Mensais0.004-0.0060.0100.008-0.0010.0060.0151.000-0.000-0.001-0.0020.0010.0020.0050.0000.0000.0000.0000.0030.0000.007
Impostos sobre a Propriedade0.0040.007-0.006-0.0040.008-0.006-0.014-0.0001.0000.0050.006-0.0130.0000.0000.0000.0050.0060.0110.0000.0170.000
Histórico de Valorização-0.0060.008-0.0030.012-0.004-0.004-0.002-0.0010.0051.000-0.003-0.0020.0150.0000.0140.0130.0040.0060.0000.0000.009
Fluxos de Caixa Anuais-0.015-0.003-0.004-0.0040.006-0.012-0.005-0.0020.006-0.0031.000-0.0150.0000.0000.0000.0000.0060.0000.0000.0000.012
Retorno sobre Investimento (ROI)0.0150.004-0.0000.002-0.0040.0070.0060.001-0.013-0.002-0.0151.0000.0060.0060.0000.0010.0050.0090.0000.0000.000
Tipo de Imóvel0.0000.0110.0010.0100.0130.0050.0000.0020.0000.0150.0000.0061.0000.0000.0000.0000.0000.0020.0000.0000.000
Localização0.0070.0000.0020.0000.0110.0000.0070.0050.0000.0000.0000.0060.0001.0000.0000.0080.0000.0190.0000.0000.006
Número de Banheiros0.0060.0070.0080.0000.0000.0000.0190.0000.0000.0140.0000.0000.0000.0001.0000.0110.0090.0070.0000.0000.002
Condição da Propriedade0.0000.0010.0080.0000.0000.0000.0000.0000.0050.0130.0000.0010.0000.0080.0111.0000.0000.0000.0190.0000.000
Amenidades0.0090.0000.0000.0070.0000.0120.0000.0000.0060.0040.0060.0050.0000.0000.0090.0001.0000.0000.0060.0000.000
Histórico de Aluguel0.0000.0000.0000.0000.0000.0070.0100.0000.0110.0060.0000.0090.0020.0190.0070.0000.0001.0000.0100.0210.000
Características do Bairro0.0000.0000.0030.0080.0110.0020.0110.0030.0000.0000.0000.0000.0000.0000.0000.0190.0060.0101.0000.0030.000
Tendências de Mercado0.0000.0000.0000.0180.0000.0000.0120.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0210.0031.0000.000
Fonte dos Dados0.0000.0050.0120.0120.0090.0000.0150.0070.0000.0090.0120.0000.0000.0060.0020.0000.0000.0000.0000.0001.000

Missing values

2023-09-05T19:47:01.540235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-05T19:47:02.091313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID da PropriedadeTipo de ImóvelLocalizaçãoCoordenadas GeográficasNúmero de QuartosNúmero de BanheirosÁrea Total (em metros quadrados)Idade da PropriedadeCondição da PropriedadeAmenidadesPreço de Venda AnteriorData de Venda AnteriorHistórico de AluguelCaracterísticas do BairroTendências de MercadoTaxas de Juros AtuaisCustos de Manutenção AnuaisTaxas de Condomínio MensaisImpostos sobre a PropriedadeHistórico de ValorizaçãoFluxos de Caixa AnuaisRetorno sobre Investimento (ROI)Data de Inclusão no DatasetFonte dos Dados
057ApartamentoCidade 8(-70.22159224490184, 3.7662768709245995)42166.18736921RuimGaragem, Vista panorâmica9770451999-02-02NãoBairro 12Decrescente5.3283023239.872395169.9782762032.2703400.3027622244.2790680.0596672023-07-16Kaggle
14374CondomínioCidade 7(61.07885709193957, 9.97770412467574)22262.28295532BoaJardim, Garagem17078982006-07-13NãoBairro 9Estável5.3519333235.091149280.7551282869.3643220.3597447924.9191270.1127642020-02-04Redfin
29163CasaCidade 8(32.30897809624264, 63.624690308488766)42166.03106937BoaPiscina, Vista panorâmica46070572013-06-22NãoBairro 16Crescente5.5158893517.205064165.0720632852.9368490.3408715058.2503240.1491712014-01-10Zillow
3687CondomínioCidade 8(83.87940579353597, 26.253839203995028)51129.82012814RuimGaragem, Vista panorâmica9353951980-05-20SimBairro 15Estável4.4991074659.19581150.4353262324.2494530.17518144992.9243790.1615892019-04-20Zillow
47095CondomínioCidade 5(-12.467080314031179, 67.49621514853379)6169.1689525MédiaPiscina, Garagem44118982014-12-10SimBairro 8Decrescente3.9404482483.899420224.2622973082.8005340.4242845246.9881530.0970452020-02-12Zillow
53415CasaCidade 10(75.24883317905176, -85.7025240922493)12116.51901226RuimPiscina, Garagem10942311994-12-10NãoBairro 11Decrescente5.9801264928.51301673.8095871981.2504230.15799538756.0102740.0974352015-11-15Redfin
63364ApartamentoCidade 9(-15.74141978357278, -12.56883642175697)21213.4647493BoaGaragem, Vista panorâmica8102052019-02-13NãoBairro 15Estável4.070654693.406103213.2520922135.9681610.40622015208.9255420.1030292019-11-01Kaggle
71214ApartamentoCidade 9(39.74314187082672, -92.1125748716351)33108.97396745ExcelentePiscina, Vista panorâmica24122452001-05-24SimBairro 8Estável3.9650144646.657870139.5631374575.9677990.3966697468.8746760.1372342019-02-07Kaggle
88103ApartamentoCidade 10(-66.59911124548321, -166.12624342436015)32168.03069440RuimVista panorâmica, Piscina14941012009-09-21NãoBairro 1Decrescente5.490240220.215861361.7768414018.9033780.23709536058.6324380.1398512016-09-26Zillow
94940CasaCidade 9(50.32013469807805, 161.69079770730303)5161.6595632RuimPiscina, Vista panorâmica42534741981-04-20SimBairro 1Crescente4.3996841650.890563114.0051753982.9118570.16723612547.0669970.0972152016-07-15Kaggle
ID da PropriedadeTipo de ImóvelLocalizaçãoCoordenadas GeográficasNúmero de QuartosNúmero de BanheirosÁrea Total (em metros quadrados)Idade da PropriedadeCondição da PropriedadeAmenidadesPreço de Venda AnteriorData de Venda AnteriorHistórico de AluguelCaracterísticas do BairroTendências de MercadoTaxas de Juros AtuaisCustos de Manutenção AnuaisTaxas de Condomínio MensaisImpostos sobre a PropriedadeHistórico de ValorizaçãoFluxos de Caixa AnuaisRetorno sobre Investimento (ROI)Data de Inclusão no DatasetFonte dos Dados
199909790ApartamentoCidade 10(50.54090016444087, -123.34798460414889)5258.96203242RuimJardim, Piscina13857851988-10-04NãoBairro 17Decrescente3.101941283.372838151.3255372538.7742670.25722519434.3430330.0918352020-08-24Redfin
199918583CasaCidade 9(44.251816328354266, 60.67223589698682)32128.72312930BoaPiscina, Jardim45873372007-10-19NãoBairro 1Decrescente3.0969412558.809785365.6221302617.7363940.40609331577.0586160.0793152023-02-27Redfin
199922424ApartamentoCidade 9(77.45269359560282, -25.763245665903042)61213.53345718BoaGaragem, Jardim25369142016-08-11NãoBairro 5Estável5.6014973906.695184338.0503604498.9412980.18335147231.7647000.1804422019-03-03Redfin
199931345CasaCidade 10(-59.76283763074659, -68.17520866595058)32169.09373413MédiaVista panorâmica, Garagem16692662012-10-21SimBairro 17Crescente5.4242271859.508339243.9779122182.5919520.47024115218.7505190.1631852013-10-05Redfin
199941437CasaCidade 8(-69.88626337174026, -84.68492829254474)32174.62755725RuimGaragem, Jardim49424192000-01-11NãoBairro 14Estável4.235872376.059024116.6581503147.6139390.23597442607.0239070.1796192013-11-15Redfin
199959988CondomínioCidade 4(-51.638915317816306, -145.36809075622068)34223.1620335ExcelenteGaragem, Jardim16024951990-10-08SimBairro 9Crescente5.6121251160.584766322.7774713148.2121860.1957282861.0330430.1628662016-01-13Kaggle
199964904CasaCidade 8(86.33047682766414, 85.04839505544237)11114.97507948BoaPiscina, Jardim10415872021-11-23NãoBairro 8Crescente2.1258041108.997023468.3485152500.1542080.26035421136.4338120.0696122018-12-18Redfin
199977104CondomínioCidade 8(83.42827159817449, 178.18691828721228)53180.65921724RuimPiscina, Garagem40889172006-07-03NãoBairro 15Crescente4.1247994059.534511110.7630574125.1556870.31842712697.1506280.1358872013-04-10Zillow
199983655CondomínioCidade 7(-22.581533079125876, 3.392759152503686)24164.28680620ExcelenteGaragem, Jardim33417842011-06-26NãoBairro 10Decrescente5.2380713251.48423491.3442364313.4950190.29105620697.9910760.1180632011-11-16Zillow
199991841CasaCidade 3(-54.29289371034657, 175.41595600176237)4192.13159950RuimPiscina, Vista panorâmica32453922020-09-23SimBairro 5Estável2.388502324.388111409.6204344720.7677170.23157446386.0089040.0714802014-07-01Zillow